变压器模型算法在中文单词意义清晰度中的应用:在中文语言中的案例研究
Linlin Li1, Juxing Li2, Hongli Wang3
1The College of Literature and Journalism, Sichuan University, Chengdu, 610000, China.
Scientific reports
|March 16, 2024
概括
本研究介绍了一种变压器融合双向长短期记忆 (BiLSTM) 模型,用于中文词语意义的明确化. 该模型达到83.71%的精度,大大提高了识别词语意义的准确性.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 词语含义的模糊性在中文语言处理中构成了重大挑战.
- 传统的方法难以捕捉复杂的语义和上下文细微差别.
研究的目的:
- 开发和评估一种新的深度学习模型,用于中文单词意义的明确化.
- 为了利用变压器和BiLSTM的优势,增强语义理解.
主要方法:
- 设计了一个混合模型,将变压器算法与双向长短期存储器 (BiLSTM) 算法融合在一起.
- 利用变压器的自我注意机制和BiLSTM的序列建模来捕获上下文信息.
- 该模型在PKU Paraphrase Bank数据集上进行了评估.
主要成果:
- 变压器融合BiLSTM模型实现了83.71%的精度,用于汉语单词意义的明确化.
- 该模型与标准的长期短期记忆算法相比,表现出更高的性能.
- 根平均平方误差低于17,损失函数值约为0.14.
结论:
- 拟议的变压器融合BiLSTM模型对于中文单词意义的明确性是准确和强大的.
- 这项研究为推进智能中文语言应用提供了有价值的见解.
- 这些发现有助于自然语言处理和深度学习应用领域.
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